Decision Trees

Comprehensive Examples & Practice Problems

📊 Example 1: Information Gain: Play Tennis Decision

Dataset

Day	Outlook	Temperature	Humidity	Wind	Play?
1	Sunny	Hot	High	Weak	No
2	Sunny	Hot	High	Strong	No
3	Overcast	Hot	High	Weak	Yes
4	Rain	Mild	High	Weak	Yes
5	Rain	Cool	Normal	Weak	Yes

Solution Approach

Calculate entropy of target: H(Play) = -P(Yes)log₂P(Yes) - P(No)log₂P(No). Then calculate information gain for each feature: IG = H(Play) - H(Play|Feature). Choose feature with highest IG for root split.

💡 Key Takeaways

Understanding the mathematical foundation helps apply the algorithm effectively in real-world scenarios.

✏️ Practice Problems

Problem 1: Apply the Algorithm

Use the method learned in this course to solve a similar problem with your own dataset.

Show Hints

Start with data preprocessing and exploration
Apply the core algorithm step-by-step
Evaluate results using appropriate metrics
Iterate and improve based on insights

Problem 2: Theoretical Understanding

Explain the mathematical principles behind Decision Trees and when to use it vs alternatives.

Show Key Points

Review the mathematical derivations in the main course and understand the assumptions, strengths, and limitations of this approach.

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